


'''simple docstring'''




import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

_a   :   List[str]												=   2




class 			_lowercase :
   def __init__(						self       :      Dict       ,   *,  # begin keyword-only arguments
       SCREAMING_SNAKE_CASE_       :      Union[str, Any]="<s>"       ,   SCREAMING_SNAKE_CASE_       :      Any="<pad>"       ,   SCREAMING_SNAKE_CASE_       :      Optional[int]="</s>"       ,   SCREAMING_SNAKE_CASE_       :      str="<unk>"       ,   SCREAMING_SNAKE_CASE_       :      int=None       ,   ) ->							List[str]:
        __snake_case      ,       __snake_case      ,       __snake_case      ,       __snake_case           =						bos, unk, pad, eos
        __snake_case           =						[]
        __snake_case           =						[]
        __snake_case           =						{}
        __snake_case           =						self.add_symbol(SCREAMING_SNAKE_CASE_      )
        __snake_case           =						self.add_symbol(SCREAMING_SNAKE_CASE_      )
        __snake_case           =						self.add_symbol(SCREAMING_SNAKE_CASE_      )
        __snake_case           =						self.add_symbol(SCREAMING_SNAKE_CASE_      )
        if extra_special_symbols:
             for s in extra_special_symbols:
                  self.add_symbol(SCREAMING_SNAKE_CASE_      )
        __snake_case           =						len(self.symbols      )
   def __eq__(						self       :      Tuple       ,   SCREAMING_SNAKE_CASE_       :      List[str]      ) ->							Optional[int]:
        return self.indices == other.indices
   def __getitem__(						self       :      List[str]       ,   SCREAMING_SNAKE_CASE_       :      List[Any]      ) ->							Tuple:
        if idx < len(self.symbols      ):
             return self.symbols[idx]
        return self.unk_word
   def __len__(						self       :      str      ) ->							Union[str, Any]:
        return len(self.symbols      )
   def __contains__(						self       :      Optional[Any]       ,   SCREAMING_SNAKE_CASE_       :      str      ) ->							Dict:
        return sym in self.indices
   @classmethod
   def a  (						cls       :      Any       ,   SCREAMING_SNAKE_CASE_       :      int      ) ->							int:
        __snake_case           =						cls()
        d.add_from_file(SCREAMING_SNAKE_CASE_      )
        return d
   def a  (						self       :      str       ,   SCREAMING_SNAKE_CASE_       :      Tuple       ,   SCREAMING_SNAKE_CASE_       :      str=1       ,   SCREAMING_SNAKE_CASE_       :      Optional[int]=False      ) ->							List[Any]:
        if word in self.indices and not overwrite:
             __snake_case           =						self.indices[word]
             __snake_case           =						self.count[idx] + n
             return idx
        else:
             __snake_case           =						len(self.symbols      )
             __snake_case           =						idx
             self.symbols.append(SCREAMING_SNAKE_CASE_      )
             self.count.append(SCREAMING_SNAKE_CASE_      )
             return idx
   def a  (						self       :      Optional[Any]       ,   SCREAMING_SNAKE_CASE_       :      List[Any]      ) ->							Optional[Any]:
        return 0





   def a  (						self       :      List[str]       ,   SCREAMING_SNAKE_CASE_       :      str      ) ->							List[str]:
        if isinstance(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      ):
             try:
                  with open(SCREAMING_SNAKE_CASE_       ,   'r'       ,   encoding='utf-8'      ) as fd:
                       self.add_from_file(SCREAMING_SNAKE_CASE_      )
             except FileNotFoundError as fnfe:
                  raise fnfe
             except UnicodeError:
                  raise Exception('Incorrect encoding detected in {}, please rebuild the dataset'.format(SCREAMING_SNAKE_CASE_      )      )
             return

        __snake_case           =						f.readlines()
        __snake_case           =						self._load_meta(SCREAMING_SNAKE_CASE_      )

        for line in lines[indices_start_line:]:
             try:
                  __snake_case      ,       __snake_case           =						line.rstrip().rsplit(' '       ,   1      )
                  if field == "#fairseq:overwrite":
                       __snake_case           =						True
                       __snake_case      ,       __snake_case           =						line.rsplit(' '       ,   1      )
                  else:
                       __snake_case           =						False
                  __snake_case           =						int(SCREAMING_SNAKE_CASE_      )
                  __snake_case           =						line
                  if word in self and not overwrite:
                       raise RuntimeError(
                           'Duplicate word found when loading Dictionary: \'{}\'. '
                           'Duplicate words can overwrite earlier ones by adding the '
                           '#fairseq:overwrite flag at the end of the corresponding row '
                           'in the dictionary file. If using the Camembert model, please '
                           'download an updated copy of the model file.'.format(SCREAMING_SNAKE_CASE_      )      )
                  self.add_symbol(SCREAMING_SNAKE_CASE_       ,   n=SCREAMING_SNAKE_CASE_       ,   overwrite=SCREAMING_SNAKE_CASE_      )
             except ValueError:
                  raise ValueError('Incorrect dictionary format, expected \'<token> <cnt> [flags]\''      )





def   _a		(lowercase__			:   List[str]						)			->			List[str]:






     """simple docstring"""
     # (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
     # e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
     __snake_case           =						dict((re.sub(r'@@$'							,   ''							,   lowercase__						), v) if k.endswith('@@'						) else (re.sub(r'$'							,   '</w>'							,   lowercase__						), v) for k, v in d.items()						)
     __snake_case           =						'<s> <pad> </s> <unk>'.split()
     # restore the special tokens
     for k in keep_keys:
          del da[f'{k}</w>']
          __snake_case           =						d[k]  # restore
     return da





def   _a		(lowercase__			:   int							,   lowercase__			:   List[str]						)			->			Union[str, Any]:






     """simple docstring"""
     # prep
     if not os.path.exists(lowercase__						):
          raise ValueError(f'path {biogpt_checkpoint_path} does not exist!'						)
     os.makedirs(lowercase__							,   exist_ok=lowercase__						)
     print(f'Writing results to {pytorch_dump_folder_path}'						)

     # handle various types of models

     __snake_case           =						os.path.join(lowercase__							,   'checkpoint.pt'						)
     if not os.path.isfile(lowercase__						):
          raise ValueError(f'path to the file {checkpoint_file} does not exist!'						)
     __snake_case           =						torch.load(lowercase__							,   map_location='cpu'						)

     __snake_case           =						chkpt['cfg']['model']

     # dicts
     __snake_case           =						os.path.join(lowercase__							,   'dict.txt'						)
     if not os.path.isfile(lowercase__						):
          raise ValueError(f'path to the file {dict_file} does not exist!'						)
     __snake_case           =						Dictionary.load(lowercase__						)
     __snake_case           =						rewrite_dict_keys(src_dict.indices						)
     __snake_case           =						len(lowercase__						)
     __snake_case           =						os.path.join(lowercase__							,   VOCAB_FILES_NAMES['vocab_file']						)
     print(f'Generating {src_vocab_file} of {src_vocab_size} records'						)
     with open(lowercase__							,   'w'							,   encoding='utf-8'						) as f:
          f.write(json.dumps(lowercase__							,   ensure_ascii=lowercase__							,   indent=lowercase__						)						)

     # merges_file (bpecodes)
     __snake_case           =						os.path.join(lowercase__							,   'bpecodes'						)
     if not os.path.isfile(lowercase__						):
          raise ValueError(f'path to the file {bpecodes_file} does not exist!'						)

     __snake_case           =						os.path.join(lowercase__							,   VOCAB_FILES_NAMES['merges_file']						)
     shutil.copyfile(lowercase__							,   lowercase__						)

     # model config
     __snake_case           =						os.path.join(lowercase__							,   'config.json'						)

     __snake_case           =						{
         'activation_dropout': args['activation_dropout'],
         'architectures': ['BioGptForCausalLM'],
         'attention_probs_dropout_prob': args['attention_dropout'],
         'bos_token_id': 0,
         'eos_token_id': 2,
         'hidden_act': args['activation_fn'],
         'hidden_dropout_prob': args['dropout'],
         'hidden_size': args['decoder_embed_dim'],
         'initializer_range': 0.02,
         'intermediate_size': args['decoder_ffn_embed_dim'],
         'layer_norm_eps': 1e-12,
         'layerdrop': args['decoder_layerdrop'],
         'max_position_embeddings': args['max_target_positions'],
         'model_type': 'biogpt',
         'num_attention_heads': args['decoder_attention_heads'],
         'num_hidden_layers': args['decoder_layers'],
         'pad_token_id': 1,
         'scale_embedding': not args['no_scale_embedding'],
         'tie_word_embeddings': args['share_decoder_input_output_embed'],
         'vocab_size': src_vocab_size,
     }

     # good hparam defaults to start with

     print(f'Generating {biogpt_model_config_file}'						)
     with open(lowercase__							,   'w'							,   encoding='utf-8'						) as f:
          f.write(json.dumps(lowercase__							,   ensure_ascii=lowercase__							,   indent=lowercase__						)						)

     # tokenizer config
     __snake_case           =						os.path.join(lowercase__							,   lowercase__						)

     __snake_case           =						{
         'bos_token': '<s>',
         'eos_token': '</s>',
         'model_max_length': 1_0_2_4,
         'pad_token': '<pad>',
         'special_tokens_map_file': None,
         'tokenizer_class': 'BioGptTokenizer',
         'unk_token': '<unk>',
     }

     print(f'Generating {biogpt_tokenizer_config_file}'						)
     with open(lowercase__							,   'w'							,   encoding='utf-8'						) as f:
          f.write(json.dumps(lowercase__							,   ensure_ascii=lowercase__							,   indent=lowercase__						)						)

     # model
     __snake_case           =						chkpt['model']

     # remove unneeded keys
     __snake_case           =						[
         'decoder.version',
     ]
     for k in ignore_keys:
          model_state_dict.pop(lowercase__							,   lowercase__						)

     __snake_case           =						list(model_state_dict.keys()						)
     for layer_name in layer_names:
          if layer_name.endswith('output_projection.weight'						):
               __snake_case           =						model_state_dict.pop(lowercase__						)
          else:
               __snake_case           =						model_state_dict.pop(lowercase__						)

     __snake_case           =						BioGptConfig.from_pretrained(lowercase__						)
     __snake_case           =						BioGptForCausalLM(lowercase__						)

     # check that it loads ok
     model_new.load_state_dict(lowercase__						)

     # save
     __snake_case           =						os.path.join(lowercase__							,   lowercase__						)
     print(f'Generating {pytorch_weights_dump_path}'						)
     torch.save(lowercase__							,   lowercase__						)

     print('Conversion is done!'						)


if __name__ == "__main__":
       _a   :   Any												=   argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--biogpt_checkpoint_path",
           default=None,
           type=str,
           required=True,
           help=(
               "Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"
               " bpecodes, etc."
           ),
       )
       parser.add_argument(
           "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
       )
       _a   :   Dict												=   parser.parse_args()
       convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)







import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


__snake_case				:      Union[str, Any]   		=			logging.getLogger(__name__)



@dataclass
class        A__(a_							):
						"""simple docstring"""






						_A    :				Optional[float]       					=			field(
						    default=0.0,  metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''}							)
						_A    :				bool       					=			field(default=a_,  metadata={'''help''': '''Whether to SortishSamler or not.'''}							)
						_A    :				bool       					=			field(
						    default=a_,  metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}							)
						_A    :				bool       					=			field(default=a_,  metadata={'''help''': '''whether to use adafactor'''}							)
						_A    :				Optional[float]       					=			field(
						    default=a_,  metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[float]       					=			field(
						    default=a_,  metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[float]       					=			field(default=a_,  metadata={'''help''': '''Dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[float]       					=			field(
						    default=a_,  metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[str]       					=			field(
						    default='''linear''',  metadata={'''help''': f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()							)}'''},  )


